R: Frequencies of labelled variables

frq {sjmisc}

R Documentation

Frequencies of labelled variables

Description

This function returns a frequency table of labelled vectors, as data frame.

Usage

frq(x, ..., sort.frq = c("none", "asc", "desc"), weight.by = NULL,
  auto.grp = NULL, show.strings = TRUE, grp.strings = NULL,
  out = c("txt", "viewer", "browser"))

Arguments

`x`	A vector or a data frame. May also be a grouped data frame (see 'Note' and 'Examples').
`...`	Optional, unquoted names of variables that should be selected for further processing. Required, if `x` is a data frame (and no vector) and only selected variables from `x` should be processed. You may also use functions like `:` or tidyselect's `select_helpers`. See 'Examples' or package-vignette.
`sort.frq`	Determines whether categories should be sorted according to their frequencies or not. Default is `"none"`, so categories are not sorted by frequency. Use `"asc"` or `"desc"` for sorting categories ascending or descending order.
`weight.by`	Vector of weights that will be applied to weight all observations. Must be a vector of same length as the input vector. Default is `NULL`, so no weights are used.
`auto.grp`	Numeric value, indicating the minimum amount of unique values in a variable, at which automatic grouping into smaller units is done (see `group_var`). Default value for `auto.group` is `NULL`, i.e. auto-grouping is off.
`show.strings`	Logical, if `TRUE`, frequency tables for character vectors will not be printed. This is useful when printing frequency tables of all variables from a data frame, and due to computational reasons character vectors should not be printed.
`grp.strings`	Numeric, if not `NULL`, groups string values in character vectors, based on their similarity. The similarity is estimated with the stringdist-package. See `group_str` for details on grouping, and that function's `maxdist`-argument to get more details on the distance of strings to be treated as equal.
`out`	Character vector, indicating whether the results should be printed to console (`out = "txt"`) or as HTML-table in the viewer-pane (`out = "viewer"`) or browser (`out = "browser"`).

Value

A list of data frames with values, value labels, frequencies, raw, valid and cumulative percentages of x.

Note

x may also be a grouped data frame (see group_by) with up to two grouping variables. Frequency tables are created for each subgroup then.

Examples

library(haven)
# create labelled integer
x <- labelled(
  c(1, 2, 1, 3, 4, 1),
  c(Male = 1, Female = 2, Refused = 3, "N/A" = 4)
)
frq(x)

x <- labelled(
  c(1:3, tagged_na("a", "c", "z"), 4:1, 2:3),
  c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"),
    "Refused" = tagged_na("a"), "Not home" = tagged_na("z"))
)
frq(x)

# in a pipe
data(efc)
library(dplyr)
efc %>%
  select(e42dep, e15relat, c172code) %>%
  frq()

# or:
# frq(efc, e42dep, e15relat, c172code)

# with grouped data frames, in a pipe
efc %>%
  group_by(e16sex, c172code) %>%
  frq(e16sex, c172code, e42dep)

# with select-helpers: all variables from the COPE-Index
# (which all have a "cop" in their name)
frq(efc, contains("cop"))

# all variables from column "c161sex" to column "c175empl"
frq(efc, c161sex:c175empl)

# for non-labelled data, variable name is printed,
# and "label" column is removed from output
data(iris)
frq(iris, Species)

# group variables with large range
frq(efc, c160age)
frq(efc, c160age, auto.grp = 5)

# group string values
## Not run: 
dummy <- efc %>% dplyr::select(3)
dummy$words <- sample(
  c("Hello", "Helo", "Hole", "Apple", "Ape",
    "New", "Old", "System", "Systemic"),
  size = nrow(dummy),
  replace = TRUE
)

frq(dummy)
frq(dummy, grp.strings = 2)
## End(Not run)

[Package sjmisc version 2.7.1 Index]